Devices for detecting an object by analyzing a captured image obtained by using an image capture device have been proposed and have been put into practical use. For example, PTL 1 discloses a technique for analyzing, in a production process of a substrate such as a printed circuit board, a captured image of the substrate to detect an object such as a scratch on the substrate. PTL 2 discloses a technique for analyzing a road monitoring video obtained by a monitoring camera to detect an object such as a car.
To perform detections as described above, it is necessary to cause an object detection device to learn analyzing of images and videos. For the learning, teacher data (referred to also as training data) is needed. The teacher data is an instance of a pair of an input and an output. The teacher data includes two types that are a positive instance and a negative instance. To cause the object detection device to correctly learn, both a positive instance and a negative instance are needed. However, it takes much time and labor to create appropriate teacher data. Therefore, several techniques for assisting in creating teacher data have been proposed.
PTL 1 (see FIG. 9), for example, discloses a technique for creating teacher data necessary to detect an object such as a scratch and the like on a substrate. The technique extracts a region having a brightness value different from that of a good article from an image of a printed circuit board, displays the extracted region on a display, and accepts a selection of the region and an input of a class (referred to also as a category) thereof from the user using a keyboard and a mouse. Specifically, the user selects one specific region among a plurality of existing regions by a click of the mouse and then selects a desired class by a click of the mouse from a pull-down menu displayed upon the selection.
Further, PTL 2 described above discloses a technique for creating teacher data necessary to detect an object such as a car and the like running on the road. The technique automatically performs a series of operations including: cutting out a region of an object from an optional captured image using a dictionary, extracting a predetermined feature amount from the region, and learning the dictionary on the basis of the extracted feature amount.